Adaptive Dissipative State Preparation through Reinforcement Learning
A preprint proposes an adaptive dissipative method for ground-state preparation in which a single ancilla qubit simulates a low-temperature bath. Reinforcement learning adjusts the ancilla's variable energy gap across repeated couplings to the system qubits.
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What this could mean
- 0–2 yearsPlausible
RL-tuned ancilla schedules could make dissipative state preparation practical for small molecular ground states on noisy intermediate-scale quantum hardware within two years.
If learned energy-gap schedules exploit problem structure better than hand-set timestep sequences, they could reduce the number of system-ancilla coupling cycles needed to reach a target fidelity, which is the main cost barrier for dissipative algorithms on current devices.
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